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Models · 7
4.12Btokens
96.65%Cache Hit Rate

LongCat-2.0 is a newly open-sourced, large-scale Mixture-of-Experts (MoE) language model featuring 1.6 trillion total parameters with approximately 48 billion activated per token. Notably, its entire training and deployment were executed entirely on AI ASIC superpods—spanning 35 trillion tokens without any irrecoverable loss spikes—demonstrating the viability of frontier-scale training on alternative hardware.

Architecturally, the model introduces LongCat Sparse Attention (LSA) for efficient 1M-token context processing, combined with an N-gram Embedding module to maximize parameter efficiency. Supported by a specialized multi-expert post-training pipeline, LongCat-2.0 excels in coding, reasoning, and autonomous agentic tasks.

Benchmark results highlight its highly competitive performance, rivaling top-tier proprietary models like GPT-5.5 and Claude Opus 4.x in evaluations such as Terminal-Bench and SWE-bench. Deeply integrated with mainstream frameworks like Claude Code, it offers developers a highly stable and efficient collaborative experience for complex repository-level edits and real-world agentic workflows.

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Input$0.75/M tokens
Output$2.95/M tokens
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58.91Btokens
82.92%Cache Hit Rate

GLM-5 is Zai’s new-generation flagship foundation model, designed for Agentic Engineering, capable of providing reliable productivity in complex system engineering and long-range Agent tasks. In terms of Coding and Agent capabilities, GLM-5 has achieved state-of-the-art (SOTA) performance in open source, with its usability in real programming scenarios approaching that of Claude Opus 4.5.

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Input$0.58-0.87/M tokens
Output$2.6-3.18/M tokens
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58.69Btokens
5.37%Cache Hit Rate

DeepSeek-V3.2 is a reasoning-first large language model released by DeepSeek as the official successor to V3.2-Exp. It is designed with a focus on agentic capabilities and integrated reasoning for tool-use scenarios.

If the request to the deepseek-reasoner model includes the tools parameter, the request will actually be processed using the deepseek-chat model.

The model introduces a new large-scale agent training data synthesis method covering over 1,800 environments and 85,000+ complex instructions. DeepSeek-V3.2 is the first model from DeepSeek to integrate thinking directly into tool-use, supporting both thinking and non-thinking modes during tool interactions.

According to DeepSeek's benchmarks, the model delivers performance comparable to GPT-5 level while balancing inference quality and output length. It is available via App, Web, and API, making it suitable for general-purpose daily use as well as complex agentic workflows.

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Input$0.293/M tokens
Output$0.4395/M tokens
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14.19Btokens
98.26%Cache Hit Rate

Kimi’s most intelligent model to date, achieving open-source SoTA performance in Agent, code, visual understanding, and a range of general intelligent tasks. It is also Kimi’s most versatile model to date, featuring a native multimodal architecture that supports both visual and text input, thinking and non-thinking modes, and dialogue and Agent tasks. Context 256k

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Input$0.95/M tokens
Output$4/M tokens
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973.93Btokens
97.01%Cache Hit Rate

In addition to DeepSeek, TencentCloud and AlibabaCloud now also offer the official 0731 version, while other providers are still using the old 0424 version. . DeepSeek V4 Pro is DeepSeek’s flagship open-weight Mixture-of-Experts model, released in preview on April 24, 2026. It features 1.6 trillion total parameters, with 49 billion activated per token, and supports a one-million-token context window. Designed for advanced reasoning, coding, STEM tasks, and autonomous agents, it delivers state-of-the-art performance among open models and rivals leading proprietary systems. The model supports both thinking and non-thinking modes and is available through DeepSeek’s applications and APIs.

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Input$0.66-1.32/M tokens
Output$1.98-3.96/M tokens
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711.31Btokens
96.64%Cache Hit Rate

DeepSeek-V4-Flash is the efficiency-oriented variant of the DeepSeek V4 series, released as a preview and open-sourced alongside the flagship V4-Pro. It is designed for developers who need the V4 generation's long-context and reasoning capability at a faster, more economical API tier. Compared with V4-Pro, V4-Flash uses smaller total parameters and active parameters, resulting in faster response times and lower API cost. It retains reasoning capability close to V4-Pro and matches V4-Pro on simple agent tasks, with a measurable gap appearing only on the most demanding agent workflows. World knowledge is slightly below V4-Pro but remains competitive within the open-source landscape.

Like V4-Pro, V4-Flash inherits the new attention mechanism built on token-dimension compression and DeepSeek Sparse Attention (DSA), supports a 1M context window as standard, and offers both thinking and non-thinking modes with a reasoning_effort parameter (high / max).

Note on migration: the legacy model names deepseek-chat and deepseek-reasoner currently route to V4-Flash in non-thinking and thinking mode respectively, and will be retired on 2026-07-24. Existing integrations should migrate to the explicit model names deepseek-v4-flash or deepseek-v4-pro. The model is accessible via OpenAI ChatCompletions and Anthropic interfaces, with weights open-sourced on Hugging Face and ModelScope.

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Input$0.22-0.44/M tokens
Output$0.66-1.32/M tokens
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161.56Btokens
92.15%Cache Hit Rate

GLM-5.1 is Zai’s new-generation flagship foundation model, designed for Agentic Engineering, capable of providing reliable productivity in complex system engineering and long-range Agent tasks. In terms of Coding and Agent capabilities, GLM-5 has achieved state-of-the-art (SOTA) performance in open source, with its usability in real programming scenarios approaching that of Claude Opus 4.5.

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Input$0.8781-1.1709/M tokens
Output$3.5126-4.098/M tokens
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